Search Results for author: Longquan Dai

Found 6 papers, 0 papers with code

Designing by Training: Acceleration Neural Network for Fast High-Dimensional Convolution

no code implementations NeurIPS 2018 Longquan Dai, Liang Tang, Yuan Xie, Jinhui Tang

Over the decades, people took a handmade approach to design fast algorithms for the Gaussian convolution.

Speeding Up the Bilateral Filter: A Joint Acceleration Way

no code implementations28 Feb 2018 Longquan Dai, Mengke Yuan, Xiaopeng Zhang

To achieve the constant-time BF whose complexity is irrelevant to the kernel size, many techniques have been proposed, such as 2D box filtering, dimension promotion, and shiftability property.

Hardware-Efficient Guided Image Filtering For Multi-Label Problem

no code implementations CVPR 2017 Longquan Dai, Mengke Yuan, Zechao Li, Xiaopeng Zhang, Jinhui Tang

In this paper we propose a hardware-efficient Guided Filter (HGF), which solves the efficiency problem of multichannel guided image filtering and yields competent results when applying it to multi-label problems with synthesized polynomial multichannel guidance.

Interpreting and Extending The Guided Filter Via Cyclic Coordinate Descent

no code implementations30 May 2017 Longquan Dai

In this paper, we will disclose that the Guided Filter (GF) can be interpreted as the Cyclic Coordinate Descent (CCD) solver of a Least Square (LS) objective function.

Segment Graph Based Image Filtering: Fast Structure-Preserving Smoothing

no code implementations ICCV 2015 Feihu Zhang, Longquan Dai, Shiming Xiang, Xiaopeng Zhang

In our SGF, we use the tree distance on the segment graph to define the internal weight function of the filtering kernel, which enables the filter to smooth out high-contrast details and textures while preserving major image structures very well.

Optical Flow Estimation Stereo Matching +1

Fully Connected Guided Image Filtering

no code implementations ICCV 2015 Longquan Dai, Mengke Yuan, Feihu Zhang, Xiaopeng Zhang

This paper presents a linear time fully connected guided filter by introducing the minimum spanning tree (MST) to the guided filter (GF).

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